Raman spectroscopy utilizing Fisher-based feature selection combined with Support Vector Machines for the characterization of breast cell lines
Raman spectroscopy utilizing Fisher-based feature selection combined with Support Vector Machines for the characterization of breast cell lines
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DOI：
10.1002/jrs.4309
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发表时间：
2013-07-01
影响因子：
2.5
通讯作者：
Pardalos, Panos M.
中科院分区：
文献类型：
--
作者：
Fenn, Michael B.;Pappu, Vijay;Pardalos, Panos M.
拉曼光谱具有显著帮助癌症的研究和诊断的潜力。信息密集、复杂的光谱产生大量数据集，其中微妙的相关性可以为生物分析和病理分类提供关键线索。因此，实施先进的数据挖掘技术是必不可少的完整，快速和准确的光谱处理。近年来，许多研究采用各种数据处理方法对拉曼光谱进行分类和生化分析。虽然，由于来自生物样本的拉曼数据集通常具有高维数和低样本数的特征，但是这些分类模型中的许多都受到过拟合。此外，试图降低维数的结果在变换的特征空间，使生物学评价的显着和歧视性的光谱特征的问题。我们已经开发了一种新的数据挖掘框架优化的拉曼数据集，称为基于Fisher的特征选择支持向量机（FFS-SVM）。该框架提供了同时监督分类和用户定义的基于Fisher准则的特征选择，减少了过拟合，并直接从原始特征空间产生显著的波数。在这里，我们研究了五个癌性和非癌性乳腺细胞系使用拉曼显微光谱和我们独特的FFS-SVM框架。我们的框架分类性能进行比较，其他几个常用的分类方法对四个分类任务。这四个任务是通过无监督聚类方法构建的，得到了研究的四种不同类别的细胞系分组（例如癌症与非癌症）。FFS-SVM实现了高分类精度和生物学显著特征的提取。根据细胞类型特异性生物相关性讨论了前十个最具鉴别力的特征。我们的框架提供了全面的细胞水平表征，并可能导致癌症生物标志物类型信息的发现，我们非正式地称之为基于拉曼光谱的生物标志物。FFS-SVM框架沿着与拉曼光谱将用于未来的研究，以调查在原位动态生物现象。版权所有（c）2013约翰威利父子有限公司
Raman spectroscopy has the potential to significantly aid in the research and diagnosis of cancer. The information dense, complex spectra generate massive datasets in which subtle correlations may provide critical clues for biological analysis and pathological classification. Therefore, implementing advanced data mining techniques is imperative for complete, rapid and accurate spectral processing. Numerous recent studies have employed various data methods to Raman spectra for classification and biochemical analysis. Although, as Raman datasets from biological specimens are often characterized by high dimensionality and low sample numbers, many of these classification models are subject to overfitting. Furthermore, attempts to reduce dimensionality result in transformed feature spaces making the biological evaluation of significant and discriminative spectral features problematic. We have developed a novel data mining framework optimized for Raman datasets, called Fisher-based Feature Selection Support Vector Machines (FFS-SVM). This framework provides simultaneous supervised classification and user-defined Fisher criterion-based feature selection, reducing overfitting and directly yielding significant wavenumbers from the original feature space. Herein, we investigate five cancerous and non-cancerous breast cell lines using Raman microspectroscopy and our unique FFS-SVM framework. Our framework classification performance is then compared to several other frequently employed classification methods on four classification tasks. The four tasks were constructed by an unsupervised clustering method yielding the four different categories of cell line groupings (e.g. cancer vs non-cancer) studied. FFS-SVM achieves both high classification accuracies and the extraction of biologically significant features. The top ten most discriminative features are discussed in terms of cell-type specific biological relevance. Our framework provides comprehensive cellular level characterization and could potentially lead to the discovery of cancer biomarker-type information, which we have informally termed Raman-based spectral biomarkers'. The FFS-SVM framework along with Raman spectroscopy will be used in future studies to investigate in-situ dynamic biological phenomena. Copyright (c) 2013 John Wiley & Sons, Ltd.